<div dir="ltr"><div class="gmail_default" style="font-family:arial,helvetica,sans-serif;font-size:small;color:#000000">Interesting that I downloaded this paper this morning also. Haven't yet looked to see what I could understand about it. From the little I know about it, this looks like it is related to the self-attention mechanism in transformers. </div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><font><u style="color:rgb(33,33,33);font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:16.5px;line-height:20px"><br></u></font></div><div dir="ltr"><font><u style="color:rgb(33,33,33);font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:16.5px;line-height:20px"> </u></font><span style="color:rgb(33,33,33);font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:16.5px;line-height:24.75px"> </span>-- Russ Abbott <br>Professor Emeritus, Computer Science<br>California State University, Los Angeles<br></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Sun, May 7, 2023 at 9:29\u202fAM Steve Smith <<a href="mailto:sasmyth@swcp.com">sasmyth@swcp.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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<p><a href="https://arxiv.org/pdf/2304.14767.pdf" target="_blank">https://arxiv.org/pdf/2304.14767.pdf</a></p>
<p>I am pretty much over my head in this literature, but continue to
be fascinated as I watch people who are not try to untangle some
explanatory power in their models...</p>
<p>The details of this analysis or framing this as <i>information
flow</i> rather than <i>static data/structure</i> is
reminiscent of some very nascent work we *tried* to do 15 years
ago, attempting to analyze/understand huge Systems Dynamics models
of Critical Infrastructure joined together/coupled to try to
predict the potential for cascading failures through these coupled
systems. The representation *as* SD models were natural for this
framing but we made only the tiniest progress IMO in extracting
hints of *explanatory* narratives. I was primarily doing
visualization on those tasks but tried to focus on clustering of
the Dual Graph/Network to find structure in the *flow* during
extreme events rather than in the engineered/designed structure of
the network itself.<br>
</p>
<p>I know there are others on this list who have worked with
complex, dynamic networks (I'm thinking of Frank's colleagues and
Causal Discovery in Graphical Models, various project Glen has
alluded to, and a wide variety of problems Stephen has related to
me over the years, but I'm sure there are plenty of others)...
I'm curious if anyone else is wading in this deep (and more to the
point, finding any traction)?<br>
</p>
<p>From the paper:<br>
</p>
<blockquote>
<blockquote>
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<p><img src="cid:187f7bb61c1128447a11"></p>
</blockquote>
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</blockquote>
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